Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2503.01028

Unified Power Flow Model for Bipolar HVDC Grids in Unbalanced Operation

Chandra Kant Jat·Francesco Giacomo Puricelli·Jef Beerten·Hakan Ergun·Dirk Van Hertem
View original source ↗Read source document ↗Export metadata ↓

Abstract

In the near future, point-to-point High Voltage Direct Current (HVDC) systems are expected to evolve into multi-terminal and meshed HVDC grids, predominantly adopting a bipolar HVDC configuration. Normally, bipolar HVDC systems operate in balanced mode, i.e., near zero current flows through metallic or ground return. However, bipolar HVDC systems can also be operated in an unbalanced mode in case of a single converter pole or line conductor outage. A steady-state analysis of the unbalanced DC network requires solving a power flow problem including various converter control modes, as the steady-state behavior of the converters is governed by their control modes. This paper presents a comprehensive and unified power flow model for the balanced and unbalanced operation of bipolar HVDC grids, including various converter control modes on the AC and DC sides of the converter, in a hybrid AC/DC system. It extends the basic control modes, developed for monopolar HVDC grids, to support the balanced as well as unbalanced operation of bipolar HVDC grids. Additionally, an AC-droop control, which defines a droop relationship between voltage magnitude and reactive power at the AC side of the converter, is incorporated into the modeling of bipolar HVDC systems. The functionality of the proposed model is demonstrated through a test case, and the power flow results are validated using PSCAD simulations. The impact of converter control modes on post-contingency system states is also investigated for single-pole contingencies. The proposed model is implemented as an open-source tool in the Julia/JuMP framework, where larger test cases demonstrate the robustness of the model and tool.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chandra Kant Jat, Francesco Giacomo Puricelli, Jef Beerten, Hakan Ergun, Dirk Van Hertem. 2025-03-02. Unified Power Flow Model for Bipolar HVDC Grids in Unbalanced Operation. https://doi.org/10.1109/tpwrs.2025.3641544

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY↗
arXiv2026

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY↗
arXiv2026

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY↗